Dust dominates high-altitude snow darkening and melt over high-mountain Asia


Westerly driven, long-range transportation of dust particles in elevated aerosol layers (EALs) is a persistent phenomenon during spring and summer over the Indian subcontinent. During the snow accumulation season, EALs transport substantial amounts of dust to the snow-covered slopes of high-mountain Asia (HMA). Here we use multiple satellite-based estimates to demonstrate a robust physical association between the EALs and dust-induced snow darkening over HMA. Results from a fully coupled atmosphere–chemistry–snow model support these observations, revealing across HMA a signature of increasing dust-induced snow darkening with surface elevation that peaks near 4,500 m. Moreover, the influence of dust on snow darkening is greater than that of black carbon above 4,000 m. Our findings suggest a discernible role of dust in the observed spatial heterogeneity of snowmelt and snowline trends over HMA and highlight an increasing contribution of dust to snowmelt as the snowline rises with warming.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Climatological spatial correlation between EALs and LAP-induced snow albedo darkening over HMA.
Fig. 2: Comparable elevational signature of EALs and ∆α values are observed over HMA.
Fig. 3: Association between dust in EALs and LAP-induced snow albedo reduction over the HMA ranges.
Fig. 4: Simulated elevational dependence of the radiative impact of dust and BC on snow albedo over HMA.
Fig. 5: Simulated time–elevation variability in dust-induced snowmelt over western HMA for December 2013–August 2014.

Data availability

MODIS data are available from https://modis.gsfc.nasa.gov/data/dataprod/. The CALLIPSO dataset used in this study can be downloaded from http://eosweb.larc.nasa.gov/. The AURA-OMI dataset used in this study can be downloaded from https://omisips1.omisips.eosdis.nasa.gov/. The MERRA-2 reanalysis data used in this study can be downloaded from http://disc.sci.gsfc.nasa.gov/daac-bin/FTPSubset2.pl. All processed data used in this study are archived at https://portal.nersc.gov/project/m1660/yang560/hma_dust/

Code availability

WRF-Chem is a community model freely available from https://github.com/wrf-model/WRF/releases. The WRF-Chem script modifications used in this study are archived at https://portal.nersc.gov/project/m1660/yang560/hma_dust/. Fig. 1a,b was prepared using ARC-GIS software. All other figures were prepared using MATLAB software. Code for data analysis and figure creation can be obtained from the corresponding author upon request.


  1. 1.

    Yao, T. et al. Recent third pole’s rapid warming accompanies cryospheric melt and water cycle intensification and interactions between monsoon and environment: multidisciplinary approach with observations, modeling, and analysis. Bull. Am. Meteorol. Soc. 100, 423–444 (2018).

    Google Scholar 

  2. 2.

    Armstrong, R. L. et al. Runoff from glacier ice and seasonal snow in High Asia: separating melt water sources in river flow. Reg. Environ. Chang. 19, 1249–1261 (2019).

    Google Scholar 

  3. 3.

    Guo, J. et al. Linking atmospheric pollution to cryospheric change in the third pole region: current progresses and future prospects. Natl Sci. Rev. 6, 796–809 (2019).

    Google Scholar 

  4. 4.

    Bolch, T. et al. in The Hindu Kush Himalaya Assessment: Mountains, Climate Change, Sustainability and People (eds Wester, P. et al.) 209–255 (Springer, 2019).

  5. 5.

    Smith, T. & Bookhagen, B. Changes in seasonal snow water equivalent distribution in high mountain Asia (1987 to 2009). Sci. Adv. 4, e1701550 (2018).

    Google Scholar 

  6. 6.

    IPCC Climate Change 2013: The Physical Science Basis. Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 33 (Cambridge Univ. Press, 2014).

  7. 7.

    Painter, T. H., Seidel, F. C., Bryant, A. C., McKenzie Skiles, S. & Rittger, K. Imaging spectroscopy of albedo and radiative forcing by light-absorbing impurities in mountain snow. J. Geophys. Res. Atmos. 118, 9511–9523 (2013).

    Google Scholar 

  8. 8.

    Qian, Y. et al. Light-absorbing particles in snow and ice: measurement and modeling of climatic and hydrological impact. Adv. Atmos. Sci. 32, 64–91 (2015).

    CAS  Google Scholar 

  9. 9.

    McKenzie Skiles, S. & Painter, T. H. Assessment of radiative forcing by light-absorbing particles in snow from in situ observations with radiative transfer modeling. J. Hydrometeorol. 19, 1397–1409 (2018).

    Google Scholar 

  10. 10.

    Qian, Y., Flanner, M. G., Leung, L. R. & Wang, W. Sensitivity studies on the impacts of Tibetan Plateau snowpack pollution on the Asian hydrological cycle and monsoon climate. Atmos. Chem. Phys. 11, 1929–1948 (2011).

    CAS  Google Scholar 

  11. 11.

    Gautam, R., Hsu, N. C., Lau, W. K. M. & Yasunari, T. J. Satellite observations of desert dust-induced Himalayan snow darkening. Geophys. Res. Lett. 40, 988–993 (2013).

    Google Scholar 

  12. 12.

    Yasunari, T. J. et al. Estimated range of black carbon dry deposition and the related snow albedo reduction over Himalayan glaciers during dry pre-monsoon periods. Atmos. Environ. 78, 259–267 (2013).

    CAS  Google Scholar 

  13. 13.

    Nair, V. S. et al. Black carbon aerosols over the Himalayas: direct and surface albedo forcing. Tellus B Chem. Phys. Meteorol. 65, 19738 (2013).

    Google Scholar 

  14. 14.

    Ménégoz, M. et al. Snow cover sensitivity to black carbon deposition in the Himalayas: from atmospheric and ice core measurements to regional climate simulations. Atmos. Chem. Phys. 14, 4237–4249 (2014).

    Google Scholar 

  15. 15.

    Ming, J. et al. Black carbon record based on a shallow Himalayan ice core and its climatic implications. Atmos. Chem. Phys. 8, 1343–1352 (2008).

    CAS  Google Scholar 

  16. 16.

    Usha, K. H., Nair, V. S. & Babu, S. S. Modeling of aerosol induced snow albedo feedbacks over the Himalayas and its implications on regional climate. Clim. Dyn. 54, 4191–4210 (2020).

    Google Scholar 

  17. 17.

    Sarangi, C. et al. Impact of light-absorbing particles on snow albedo darkening and associated radiative forcing over high-mountain Asia: high-resolution WRF-Chem modeling and new satellite observations. Atmos. Chem. Phys. 19, 7105–7128 (2019).

    CAS  Google Scholar 

  18. 18.

    Svensson, J. et al. Light-absorption of dust and elemental carbon in snow in the Indian Himalayas and the Finnish Arctic. Atmos. Meas. Tech. 11, 1403–1416 (2018).

    CAS  Google Scholar 

  19. 19.

    Kaspari, S., Painter, T. H., Gysel, M., Skiles, S. M. & Schwikowski, M. Seasonal and elevational variations of black carbon and dust in snow and ice in the Solu-Khumbu, Nepal and estimated radiative forcings. Atmos. Chem. Phys. 14, 8089–8103 (2014).

    Google Scholar 

  20. 20.

    Bonasoni, P. et al. Atmospheric brown clouds in the Himalayas: first two years of continuous observations at the Nepal Climate Observatory-Pyramid (5079 m). Atmos. Chem. Phys. 10, 7515–7531 (2010).

    CAS  Google Scholar 

  21. 21.

    Vaishya, A. et al. Large contrast in the vertical distribution of aerosol optical properties and radiative effects across the Indo-Gangetic Plain during the SWAAMI–RAWEX campaign. Atmos. Chem. Phys. 18, 17669–17685 (2018).

    CAS  Google Scholar 

  22. 22.

    Sarangi, C., Tripathi, S. N., Mishra, A. K., Goel, A. & Welton, E. J. Elevated aerosol layers and their radiative impact over Kanpur during monsoon onset period. J. Geophys. Res. Atmos. 121, 7936-7957 (2016).

  23. 23.

    Gautam, R., Hsu, N. C. & Lau, K.-M. Premonsoon aerosol characterization and radiative effects over the Indo-Gangetic Plains: implications for regional climate warming. J. Geophys. Res.—Atmos. 115, D17208 (2010).

    Google Scholar 

  24. 24.

    Mishra, A. K. & Shibata, T. Climatological aspects of seasonal variation of aerosol vertical distribution over central Indo-Gangetic belt (IGB) inferred by the space-borne lidar CALIOP. Atmos. Environ. 46, 365–375 (2012).

    CAS  Google Scholar 

  25. 25.

    Liu, Z. et al. Airborne dust distributions over the Tibetan Plateau and surrounding areas derived from the first year of CALIPSO lidar observations. Atmos. Chem. Phys. 8, 5045–5060 (2008).

    CAS  Google Scholar 

  26. 26.

    Das, S., Dey, S., Dash, S. K. & Basil, G. Examining mineral dust transport over the Indian subcontinent using the regional climate model, RegCM4.1. Atmos. Res. 134, 64–76 (2013).

    CAS  Google Scholar 

  27. 27.

    Warren, S. G. & Wiscombe, W. J. A model for the spectral albedo of snow. II: snow containing atmospheric aerosols. J. Atmos. Sci. 37, 2734–2745 (1980).

    Google Scholar 

  28. 28.

    Warren, S. G. Optical properties of snow. Rev. Geophys. 20, 67–89 (1982).

    Google Scholar 

  29. 29.

    Dang, C., Fu, Q. & Warren, S. G. Effect of snow grain shape on snow albedo. J. Atmos. Sci. 73, 3573–3583 (2016).

    Google Scholar 

  30. 30.

    Hansen, J. & Nazarenko, L. Soot climate forcing via snow and ice albedos. Proc. Natl Acad. Sci. USA 101, 423–428 (2004).

    CAS  Google Scholar 

  31. 31.

    Painter, T. H. et al. Response of Colorado River runoff to dust radiative forcing in snow. Proc. Natl Acad. Sci. USA 107, 17125–17130 (2010).

    CAS  Google Scholar 

  32. 32.

    Skiles, S. M., Painter, T. H., Deems, J. S., Bryant, A. C. & Landry, C. C. Dust radiative forcing in snow of the Upper Colorado River Basin: 2. Interannual variability in radiative forcing and snowmelt rates. Water Resour. Res. 48, W07522 (2012).

    Google Scholar 

  33. 33.

    Skiles, S. M. K. & Painter, T. Daily evolution in dust and black carbon content, snow grain size, and snow albedo during snowmelt, Rocky Mountains, Colorado. J. Glaciol. 63, 118–132 (2017).

    Google Scholar 

  34. 34.

    Di Mauro, B. et al. Mineral dust impact on snow radiative properties in the European Alps combining ground, UAV, and satellite observations. J. Geophys. Res. Atmos. 120, 6080–6097 (2015).

    Google Scholar 

  35. 35.

    Dumont, M. et al. In situ continuous visible and near-infrared spectroscopy of an alpine snowpack. Cryosph. 11, 1091–1110 (2017).

    Google Scholar 

  36. 36.

    Huang, J. et al. Dust and black carbon in seasonal snow across northern China. Bull. Am. Meteorol. Soc. 92, 175–181 (2010).

    Google Scholar 

  37. 37.

    Wang, X. et al. Observations and model simulations of snow albedo reduction in seasonal snow due to insoluble light-absorbing particles during 2014 Chinese survey. Atmos. Chem. Phys. 17, 2279–2296 (2017).

    CAS  Google Scholar 

  38. 38.

    Zhang, Y. et al. Black carbon and mineral dust in snow cover on the Tibetan Plateau. Cryosph. 12, 413–431 (2018).

    Google Scholar 

  39. 39.

    Warren, S. G. Can black carbon in snow be detected by remote sensing? J. Geophys. Res. Atmos. 118, 779–786 (2013).

    CAS  Google Scholar 

  40. 40.

    Flanner, M. G., Zender, C. S., Randerson, J. T. & Rasch, P. J. Present-day climate forcing and response from black carbon in snow. J. Geophys. Res. Atmos. 112, D11202 (2007).

    Google Scholar 

  41. 41.

    Doherty, S. J. et al. Observed vertical redistribution of black carbon and other insoluble light-absorbing particles in melting snow. J. Geophys. Res. Atmos. 118, 5553–5569 (2013).

    Google Scholar 

  42. 42.

    Painter, T. H., Bryant, A. C. & McKenzie Skiles, S. Radiative forcing by light absorbing impurities in snow from MODIS surface reflectance data. Geophys. Res. Lett. 39, L17502 (2012).

    Google Scholar 

  43. 43.

    Hadley, O. L. & Kirchstetter, T. W. Black-carbon reduction of snow albedo. Nat. Clim. Chang. 2, 437–440 (2012).

    CAS  Google Scholar 

  44. 44.

    Brun, F., Berthier, E., Wagnon, P., Kääb, A. & Treichler, D. A spatially resolved estimate of High Mountain Asia glacier mass balances from 2000 to 2016. Nat. Geosci. 10, 668 (2017).

    CAS  Google Scholar 

  45. 45.

    Zhao, H., Yang, W., Yao, T., Tian, L. & Xu, B. Dramatic mass loss in extreme high-elevation areas of a western Himalayan glacier: observations and modeling. Sci. Rep. 6, 30706 (2016).

    CAS  Google Scholar 

  46. 46.

    Ji, Z. M. Modeling black carbon and its potential radiative effects over the Tibetan Plateau. Adv. Clim. Chang. Res. 7, 139–144 (2016).

    Google Scholar 

  47. 47.

    Xu, J. et al. The melting Himalayas: cascading effects of climate change on water, biodiversity, and livelihoods. Conserv. Biol. 23, 520–530 (2009).

    CAS  Google Scholar 

  48. 48.

    Ghatak, D., Sinsky, E. & Miller, J. Role of snow-albedo feedback in higher elevation warming over the Himalayas, Tibetan Plateau and Central Asia. Environ. Res. Lett. 9, 114008 (2014).

  49. 49.

    Bormann, K. J., Brown, R. D., Derksen, C. & Painter, T. H. Estimating snow-cover trends from space. Nat. Clim. Change 8, 924–928 (2018).

    Google Scholar 

  50. 50.

    Ming, J., Xiao, C., Du, Z. & Yang, X. An overview of black carbon deposition in High Asia glaciers and its impacts on radiation balance. Adv. Water Resour. 55, 80–87 (2013).

    CAS  Google Scholar 

  51. 51.

    Painter, T. H. et al. Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sens. Environ. 113, 868–879 (2009).

    Google Scholar 

  52. 52.

    Rittger, K., Painter, T. H. & Dozier, J. Assessment of methods for mapping snow cover from MODIS. Adv. Water Resour. 51, 367–380 (2013).

    Google Scholar 

  53. 53.

    Dozier, J., Painter, T. H., Rittger, K. & Frew, J. E. Time–space continuity of daily maps of fractional snow cover and albedo from MODIS. Adv. Water Resour. 31, 1515–1526 (2008).

    Google Scholar 

  54. 54.

    Rittger, K., Bair, E. H., Kahl, A. & Dozier, J. Spatial estimates of snow water equivalent from reconstruction. Adv. Water Resour. 94, 345–363 (2016).

    Google Scholar 

  55. 55.

    Chand, D. et al. Quantifying above-cloud aerosol using spaceborne lidar for improved understanding of cloudy-sky direct climate forcing. J. Geophys. Res. Atmos. 113, D13206 (2008).

    Google Scholar 

  56. 56.

    Winker, D. M. et al. The CALIPSO mission. Bull. Am. Meteorol. Soc. 91, 1211–1230 (2010).

    Google Scholar 

  57. 57.

    Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).

    Google Scholar 

  58. 58.

    Molod, A., Takacs, L., Suarez, M. & Bacmeister, J. Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2. Geosci. Model Dev. 8, 1339–1356 (2015).

    Google Scholar 

  59. 59.

    Buchard, V. et al. Using the OMI aerosol index and absorption aerosol optical depth to evaluate the NASA MERRA Aerosol Reanalysis. Atmos. Chem. Phys. 15, 5743–5760 (2015).

    CAS  Google Scholar 

  60. 60.

    Derber, J. C., Parrish, D. F. & Lord, S. J. The New Global Operational Analysis System at the National Meteorological Center. Weather Forecast. 6, 538–547 (1991).

    Google Scholar 

  61. 61.

    Herman, J. R. et al. Global distribution of UV-absorbing aerosols from Nimbus 7/TOMS data. J. Geophys. Res. Atmos. 102, 16911–16922 (1997).

    CAS  Google Scholar 

  62. 62.

    Huang, J., Ge, J. & Weng, F. Detection of Asia dust storms using multisensor satellite measurements. Remote Sens. Environ. 110, 186–191 (2007).

    Google Scholar 

  63. 63.

    Sun, H., Liu, X. & Pan, Z. Direct radiative effects of dust aerosols emitted from the Tibetan Plateau on the East Asian summer monsoon—a regional climate model simulation. Atmos. Chem. Phys. 17, 13731–13745 (2017).

    CAS  Google Scholar 

  64. 64.

    Zaveri, R. A., Easter, R. C., Fast, J. D. & Peters, L. K. Model for simulating aerosol interactions and chemistry (MOSAIC). J. Geophys. Res. Atmos. 113, D13204 (2008).

    Google Scholar 

  65. 65.

    Flanner, M. G., Liu, X., Zhou, C., Penner, J. E. & Jiao, C. Enhanced solar energy absorption by internally-mixed black carbon in snow grains. Atmos. Chem. Phys. 12, 4699–4721 (2012).

    CAS  Google Scholar 

  66. 66.

    Zhao, C. et al. Simulating black carbon and dust and their radiative forcing in seasonal snow: a case study over North China with field campaign measurements. Atmos. Chem. Phys. 14, 11475–11491 (2014).

    Google Scholar 

Download references


This research was primarily supported by the NASA High Mountain Asia Project. NASA Applied Sciences 2017 GEO Award 80NSSC18K0427 supported part of this work. C.S. thanks M. Alaa for his inputs and help with ARC-GIS for plotting Fig. 1a and B. C.S. is also partially supported by the New Faculty Initiation Grant project number CE/20-21/065/NFIG/008961 from IIT Madras. The Pacific Northwest National Laboratory (PNNL) is operated for DOE by Battelle Memorial Institute under contract DE-AC06-76RLO 1830. Part of this work was performed at the Jet Propulsion Laboratory, California Institute of Technology under contract with NASA.

Author information




C.S. and Y.Q. conceived the study. C.S. did the analysis and wrote the initial manuscript under the mentorship of Y.Q. Satellite retrieval of MODSCAG and MODRFFS products were provided by K.R., K.J.B. and T.H.P. The CALIPSO data were provided by D.C. All authors provided inputs during manuscript preparation and revision.

Corresponding authors

Correspondence to Chandan Sarangi or Yun Qian.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–11.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sarangi, C., Qian, Y., Rittger, K. et al. Dust dominates high-altitude snow darkening and melt over high-mountain Asia. Nat. Clim. Chang. (2020). https://doi.org/10.1038/s41558-020-00909-3

Download citation


Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing